Rover vs. Microsoft Sentinel

Rover and Microsoft Sentinel comparison.

Investigate further back—without restoring archived logs.

With Rover, your security data and indexes stay in your own object storage. Search retained history without restoring archived logs or maintaining always-on search clusters.

Microsoft Sentinel also supports historical investigations. Its analytics tier keeps 90 days of Sentinel data by default and can be extended up to two years at added cost, while the Sentinel data lake retains data for up to 12 years. Data lake queries are slower and billed per gigabyte scanned. The difference lies in how historical access is delivered and priced.

Make accessible evidence—not storage duration alone—the measure of your retention strategy.

Get started in hours. Simplify ongoing operations.

Connect Rover through HTTP ingestion or data already in your object storage. Get started in hours, with schema-on-read to reduce upfront data-modeling work and no persistent search clusters to provision. Timing depends on data readiness, access approvals, and integration scope. Initial setup does not represent a full enterprise migration.

Microsoft Sentinel is a managed service built on a Log Analytics workspace, with built-in connectors for Microsoft sources. Other sources are onboarded through Content Hub solutions, the Azure Monitor Agent, and data collection rules, so scope depends on how much of your estate runs outside Microsoft. Sentinel in the Azure portal retires on March 31, 2027, so deployments should plan around the Microsoft Defender portal.

Evaluate onboarding by the time it takes to establish usable security coverage—not simply ingest the first log.

Give every investigation more context.

Rover connects historical search, entity relationships, and AI-led investigation to build evidence-backed cases. Help your team trace related activity, reconstruct attack timelines, and understand what happened before an alert.

Sentinel combines incident investigation with user and entity behavior analytics (UEBA), playbook-based automation, and AI assistance through Security Copilot and the Sentinel MCP server. Each user can run five concurrent analytics queries, or two on the Basic and Auxiliary tiers. Extra queries wait in a queue that times out after three minutes, which limits AI agents that run many searches in parallel.

Compare investigation quality, analyst effort, and the response workflows your SOC needs—not simply the presence of AI.

Detection rules: focus on coverage, not just rule count.

Your detection strategy should be driven by risk. The question is not simply how many rules you can enable, but how reliably they run as coverage grows.

Our Signal Mesh engine continuously updates detection state as telemetry arrives. A separate, on-demand query engine handles historical investigations, giving continuous detection and investigative search distinct execution paths.

In Sentinel, scheduled analytics rules run KQL queries on fixed intervals of five minutes to 14 days, with a built-in five-minute delay to allow for ingestion latency. Near-real-time rules run every minute but are limited to 50 enabled rules, and data in the Sentinel data lake is excluded from analytics rules. Maintaining timely detection requires attention to rule frequency, query efficiency, and which data stays in the analytics tier.

Evaluate detection reliability under load—not just the number of enabled rules.

Understand the annual cost of visibility.

Our pricing is based on daily ingestion volume, includes multi-year retention, and adds $0.01 per query.

Sentinel's analytics tier is priced per gigabyte ingested, with retention charges for keeping data beyond the default window. Data lake storage costs less, but every data lake query is billed per gigabyte scanned.

For a 1 TB/day deployment, compare annual costs using the same retention period and investigation workload. Include applicable storage, implementation, support, and administration costs—not just the subscription.